Mmcformer: Missing modality compensation transformer for brain tumor segmentation
S Karimijafarbigloo, R Azad… - … Imaging with Deep …, 2024 - proceedings.mlr.press
Human brain tumours and more specifically gliomas are amongst the most life-threatening
cancers which usually arise from abnormal growth of the glial stem cells. In practice …
cancers which usually arise from abnormal growth of the glial stem cells. In practice …
ACN: adversarial co-training network for brain tumor segmentation with missing modalities
Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically
relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities …
relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities …
M3AE: multimodal representation learning for brain tumor segmentation with missing modalities
Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …
D2-Net: Dual Disentanglement Network for Brain Tumor Segmentation With Missing Modalities
Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …
Multi-modal brain tumor segmentation via missing modality synthesis and modality-level attention fusion
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and
analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and …
analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and …
Latent correlation representation learning for brain tumor segmentation with missing MRI modalities
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …
Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network
T Zhou - Pattern Recognition, 2023 - Elsevier
Accurate brain tumor segmentation is an essential step for clinical diagnosis and surgical
treatment. Multimodal brain tumor segmentation strongly relies on an effective fusion method …
treatment. Multimodal brain tumor segmentation strongly relies on an effective fusion method …
NestedFormer: Nested modality-aware transformer for brain tumor segmentation
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate
brain tumors by providing rich complementary information. Previous multi-modal MRI …
brain tumors by providing rich complementary information. Previous multi-modal MRI …
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain tumor
segmentation. However, it's common to have missing imaging modalities in clinical practice …
segmentation. However, it's common to have missing imaging modalities in clinical practice …
RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation
Most existing brain tumor segmentation methods usually exploit multi-modal magnetic
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
resonance imaging (MRI) images to achieve high segmentation performance. However, the …